UNIVERSITI PUTRA MALAYSIA UPMpsasir.upm.edu.my/id/eprint/70351/1/FK 2016 57 IR.pdfbagaimanapun, hubungan tidak linear antara arus dan voltan bagi sistem fotovolta adalah isu mencabar

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UNIVERSITI PUTRA MALAYSIA

DUAL SEARCH MAXIMUM POWER POINT ALGORITHM BASED ON MATHEMATICAL ANALYSIS UNDER PARTIALLY- SHADED

CONDITIONS

SHAHROOZ HAJIGHORBANI

FK 2016 57

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DUAL SEARCH MAXIMUM POWER POINT ALGORITHM BASED ON

MATHEMATICAL ANALYSIS UNDER PARTIALLY- SHADED CONDITIONS

By


Thesis Submitted to the School of Graduate Studies. Universiti Putra Malaysia, in

Fulfillment of the Requirements for the Degree of Doctor of Philosophy

July 2016

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COPYRIGHT

All material contained within the thesis, including without limitation text, logos, icons,

photographs and all other artwork, is copyright material of Universiti Putra Malaysia

unless otherwise stated. Use may be made of any material contained within the thesis

for non-commercial purposes from the copyright holder. Commercial use of material

may only be made with the express, prior, written permission of Universiti Putra

Malaysia.

Copyright © Universiti Putra Malaysia.

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DEDICATION

I would like to dedicate this thesis to my parents for their endless love, support, and

encouragement. Thank you both for giving me strength to reach for the starts and chase

my dreams. My sister, Neda, brother-in-law, Hamid, nephew, Arshan, and best friend,

Alireza Azadpour, deserve my wholehearted thanks as well.

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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment of

the requirement for the Doctor of Philosophy

DUAL SEARCH MAXIMUM POWER POINT ALGORITHM BASED ON

MATHEMATICAL ANALYSIS UNDER PARTIALLY SHADED CONDITIONS

By


July 2016

Chairman : Mohd Amran Mohd Radzi, PhD

Faculty : Engineering

Solar energy has drawn much attention in recent years because of high demand for

green energy resources. Electrical power can be generated by using semiconductors in

photovoltaic (PV) cells to convert solar irradiance into DC current. Each PV module

has its own optimum point at which the power delivered from the PV is at its

maximum value. Since the initial cost for using PV is high, it is essential to make the

PV module to operate at its maximum power point (MPP). However, the non-linear

relation between current and voltage for the PV system is a challengeable issue that

results in a unique MPP for its power-voltage (P-V) curve. Under uniform conditions

or without shading, there is a unique MPP on the P-V curve. By changing the

irradiance and temperature, the value of MPP will be changed. The PV system is

troubled with the weakness of nonlinearity between current and voltage under partially

shaded conditions (PSCs). Under PSCs, there are multi-peak powers. Only one of these

peak powers has the highest power, which is called global maximum power point

(GMPP), and other peak powers are the local maximum power point (LMPP).

The maximum power point tracking (MPPT) algorithms under PSCs can be

categorized generally in two groups. In the first group, the conventional techniques are

combined with other techniques and the second group is based on the optimization

methods. One of the main challenges of MPPT techniques under PSCs is ability of the

algorithms to find the GMPP faster with minimal oscillation in power. Moreover, it is

very important that the algorithms should be general and not so complicated which

could be implemented for all systems.

Therefore, this research presents design and development of a novel method, which is

called dual search maximum power point (DSMPP) algorithm, for tracking the GMPP

under PSCs. The proposed method is based on mathematical analysis that reduces the

search zone and simultaneously identifies the possible MPPs in the specified zone that

leads to determining the GMPP in minimum time. In this work, the perturb and

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observation (P&O) method based on duty cycle adjustment is introduced, which is

modified to increase speed of the search and also to reduce the oscillation.

The simulation and experimental works have been performed to investigate behavior

and performance of the proposed algorithm. The PV array in series-parallel (SP)

configuration is considered as an input of the standalone system and mathematical

model of this PV array under PSC has been developed. Moreover, the load sizing

method for PSCs is also presented to avoid controller failure when detecting the

GMPP. In evaluation part, the DSMPP algorithm has been compared with two other

methods.

According to both simulation and experimental results, by implementing the DSMPP

technique, the GMPP can be obtained faster. Moreover, the oscillation in power is

reduced significantly. Interestingly, the experimental results under different irradiances

also show that the proposed algorithm can detect the GMPP faster in comparison with

other methods. The significant reduction of oscillation in power is observed to be due

to implementation of the modified P&O.

As a conclusion, the DSMPP algorithm has successfully been performed to detect the

GMPP under PSCs in minimum time, with low oscillation in power, and high accuracy

as detecting the GMPP for different scenarios of shadowing.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

ALGORITMA DUAL CARIAN TITIK KUASA MAKSIMUM BERDASARKAN

ANALISIS MATEMATIK DI BAWAH KEADAAN SEPARA TEDUHAN

Oleh


Julai 2016

Pengerusi : Mohd Amran Mohd Radzi, PhD

Fakulti : Kejuruteraan

Tenaga suria telah menarik banyak perhatian dalam beberapa tahun kebelakangan ini

kerana permintaan yang tinggi untuk sumber tenaga hijau. Kuasa elektrik boleh

dihasilkan dengan menggunakan semikonduktor dalam sel fotovolta untuk menukar

sinaran suria kepada arus DC. Setiap modul fotovolta mempunyai titik optimum sendiri

yang mana kuasa yang dibekal daripada fotovolta adalah ada nilai maksimumnya. Oleh

sebab kos permulaan menggunakan fotovolta adalah tinggi, adalah penting untuk

memastikan modul fotovolta beroperasi pada titik kuasa maksimumnya. Walau

bagaimanapun, hubungan tidak linear antara arus dan voltan bagi sistem fotovolta

adalah isu mencabar yang membuatkan wujudnya titik kuasa maksimum yang unik

bagi lengkung kuasa-voltannya. Di bawah keadaan seragam atau tanpa teduhan,

terdapat satu titik kuasa maksimum pada lengkung kuasa-voltan. Dengan menukar

sinaran dan suhu, nilai titik kuasa maksimum akan bertukar. Sistem fotovolta

disukarkan lagi dengan kelemahan ketaklelurusan antara arus dan voltan di bawah

keadaan separa teduhan. Di bawah keadaan separa teduhan, terdapat pelbagai kuasa

puncak. Hanya satu daripada kuasa puncak ini mengandungi kuasa tertinggi, yang

dipanggil titik kuasa maksimum global, dan kuasa puncak yang lain adalah titik kuasa

maksimum tempatan.

Algoritma titik penjejakan titik kuasa maksimum di bawah keadaan separa teduhan

boleh dikategorikan umumnya dalam dua kumpulan. Dalam kumpulan pertama, teknik

konvensional akan digabungkan dengan teknik lain dan kumpulan kedua berdasarkan

kepada kaedah pengoptimuman. Salah satu cabaran bagi teknik titik penjejakan kuasa

maksimum di bawah keadaan separa teduhan adalah kebolehan algoritma berkenaan

untuk mencari titik kuasa maksimum global lebih pantas dengan ayunan minima dalam

kuasa. Tambahan lagi, adalah sangat penting bagi algoritma berkenaan bersifat umum

dan tidak terlalu rumit yang boleh digunakan pada semua sistem.

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Oleh itu, penyelidikan ini membentangkan reka bentuk dan pembangunan satu kaedah

novel, yang dipanggil algoritma carian dual titik kuasa maksimum, untuk menjejak

titik kuasa maksimum global di bawah keadaan separa teduhan. Kaedah yang

dicadangkan adalah berdasarkan analisis matematik yang mengurangkan zon pencarian

dan sekaligus mengenal pasti titik kuasa maksimum yang mungkin dalam zon

ditetapkan yang membawa kepada penemuan titik kuasa maksimum global dalam masa

yang minima. Dalam kerja ini, kaedah usik dan cerap berdasarkan pelarasan kitaran

tugas diperkenalkan, yang diubah suai untuk meningkatkan kelajuan carian dan juga

mengurangkan ayunan.

Kerja simulasi dan eksperimen telah dilaksanakan untuk menilai kelakuan dan prestasi

algoritma yang dicadangkan. Jajaran fotovolta dalam susunan sesiri-selari ditetapkan

sebagai masukan kepada sistem berdiri sendiri dan model matematik jajaran fotovolta

ini di bawah keadaan separa teduhan telah dibangunkan. Tambahan lagi, kaedah

pensaizan beban bagi keadaan separa teduhan turut dibentangkan untuk mengelak

kegagalan pengawal apabila mengesan titik kuasa maksimum global. Dalam bahagian

penilaian, algoritma carian dual titik kuasa maksimum telah dibandingkan dengan dua

kaedah lain.

Berdasarkan kedua-dua keputusan simulasi dan eksperimen, dengan melaksanakan

teknik dual carian titik kuasa maksimum, titik kuasa maksimum global boleh dicapai

dengan lebih pantas. Tambahan lagi, ayunan dalam kuasa berkurang dengan ketara.

Menariknya, keputusan eksperimen di bawah sinaran berlainan juga menunjukkan

algoritma yang dicadangkan boleh mengesan titik kuasa maksimum global dengan

lebih pantas jika dibandingkan dengan kaedah lain. Pengurangan ketara pada ayunan

dalam kuasa dapat dilihat oleh sebab perlaksanaan usik dan cerap yang diubah suai.

Kesimpulannya, algoritma dual carian titik kuasa maksium telah berjaya beroperasi

untuk mengesan titik kuasa maksimum global di bawah keadaan separa teduhan dalam

masa yang minima, dengan ayunan rendah dalam kuasa, dan ketepatan tinggi dalam

mengesan titik kuasa maksimum global bagi senario teduhan yang berlainan.

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ACKNOWLEDGEMENTS

First and foremost, praises and thanks to the God, for his showers of blessings

throughout my research work to complete the research successfully.

I would like to sincerely thank my supervisor, Assoc. Prof. Dr. Mohd Amran Mohd

Radzi, for his guidance and support throughout this study, and especially for his

confidence in me. I have been extremely lucky to have a supervisor who cared so much

about my work, and who responded to my questions and queries so promptly. Without

his inspirational guidance, his enthusiasm, his encouragements, his unselfish help, I

could never finish my doctoral work. For me, he is not only a teacher, but also a

lifetime friend and advisor.

My special thanks go also to the members of my advisory committee, Prof. Mohd

Zainal Abidin Ab.Kadir and Assoc. Prof. Dr. Suhaidi Shafie for their guidance and

helpful discussions.

I would like to thank all lecturers at the Department of Electrical and Electronic

Engineering, Universiti Putra Malaysia (UPM) who supported and helped me during

my work. I greatly appreciate the Centre for Advanced Power and Energy Research

(CAPER) and Department of Electrical and Electronic Engineering, Faculty of

Engineering Universiti Putra Malaysia, for their contribution in facilitating smoothly

successful completion of the research work alongside with other co-researchers in the

Centre.

This thesis is only a beginning of my journey.

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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been

accepted as fulfillment of the requirement for the Doctor of Philosophy. The members

of the Supervisory Committee were as follows:

Mohd Amran Mohd Radzi, PhD

Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Mohd Zainal Abidin Ab Kadir, PhD

Professor



(Member)

Suhaidi Shafie, PhD

Associate Professor



(Member)

BUJANG BIN KIM HUAT, PhD

Professor and Dean

School of Graduate Studies


Date:

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Declaration by graduate student

I hereby confirm that:

This thesis is my original work;

Quotations, illustrations and citations have been duly referenced;

This thesis has not been submitted previously or concurrently for any other degree

at any other institutions;

Intellectual properties from the thesis and copyright of the thesis are fully – owned

by Universiti Putra Malaysia, as according to the University Putra Malaysia

(Research) Rules 2012;

Written permission must be obtained from supervisor and the office of Deputy

Vice-Chancellor (Research and Innovation) before thesis is published ( in form of

written, printed or in electronic form) including books, journals, modules,

proceedings, popular writings, seminar papers, manuscripts, posters, reports,

lecture notes, learning modules or any other materials as stated in Universiti Putra

Malaysia (Research) Rules 2012;

There is no plagiarism or data falsification in the thesis, and scholarly integrity is

upheld as according to the University Putra Malaysia (Graduate Studies) Rules

2003 (Revision 2012-2013) and the University Putra Malaysia (Research) Rules

2012. The thesis has undergone plagiarism detection software.

Signature:_____________________ Date______________________

Name and Matric No.: Shahrooz Hajighorbani, GS36148

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Declaration by member of Supervisory Committee

This is to confirm that:

The research conducted and the writing of the thesis was under our supervision;

Supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate

Studies) Rules 2003 (Revision 2012 – 2013) are adhered to.

Signature:

Name of

Chairman of

Supervisory

Committee: Associate Professor Dr. Mohd Amran Mohd Radzi

Signature:

Name of

Member of

Supervisory

Committee: Professor Dr. Mohd Zainal Abidin Ab Kadir

Signature:

Name of

Member of

Supervisory

Committee: Associate Professor Dr. Suhaidi Shafie

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TABLE OF CONTENTS

Page

ABSTRACT i

ABSTRAK iii

ACKNOWLEDGEMENTS v

APPROVAL vi

DECLARATION viii

LIST OF TABLES xii

LIST OF FIGURES xiii

LIST OF APPENDICES xviii

LIST OF ABBREVIATIONS xix

CHAPTER

1 INTRODUCTION 1

1.1 Research Background 1

1.2 Problem Statement 4

1.3 Aim and Objectives 5

1.4 Scope and Limitations 5

1.5 Thesis Organization 6

2 LITERATURE REVIEW 7

2.1 Introduction 7

2.2 Photovoltaic (PV) Technology 7

2.2.1 Solar Cell 8

2.2.2 Types of PV Cells 9

2.2.3 Equivalent Circuit of Solar Cell 10

2.2.4 PV Module Model 12

2.2.5 PV Array Model Under Uniform Condition 13

2.2.6 PV Array Model Under Partially shaded Condition 18

2.3 DC-DC Converter 19

2.3.1 Types of DC-DC converters 19

2.3.2 DC-DC Boost Converter 22

2.4 Maximum Power Point Tracking (MPPT) 26

2.4.1 Concept of MPPT 26

2.4.2 Types of MPPT Algorithms 30

2.4.2.1 Perturb and Observe (P&O) 31

2.4.2.1.1 Principle of Operation 31

2.4.2.1.2 Previous Important Works on P&O 32

2.4.2.2 Incremental Conductance (IC) 36


2.4.2.2.2 Previous Important Works on IC 37

2.4.2.3 Fuzzy Logic (FL) 40


2.4.2.3.2 Previous Important Works on FL 41

2.4.2.4 Artificial Neural Network (ANN) 42


2.4.2.4.2 Previous Important Works on ANN 43

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2.4.2.5 Particle Swarm Optimization (PSO) 44


2.4.2.5.2 Previous Important Works on PSO 45

2.4.2.6 Ant Colony Optimization (ACO) 46


2.4.2.6.2 Previous Important Works on ACO 47

2.4.2.7 Fibonacci Search (FS) 47


2.4.2.7.2 Previous Important Works on FS 49

2.4.2.8 Differential Evaluation (DE) 50


2.4.2.8.2 Previous Important Works on DE 51

2.4.2.9 Flashing Fireflies (FA) and Chaos Algorithm 51


2.4.2.9.2 Previous Important Works on FA 52

2.5 Summary 55

3 METHODOLOGY 56

3.1 Introduction 56

3.2 Simulation Work 58

3.2.1 Modeling of PV Module and Array 58

3.2.2 Boost DC-DC Converter 61

3.2.2.1 Design of DC-DC Converter 61

3.2.2.2 Simulation of DC-DC Converter Circuit 65

3.2.3 Development of MPPT Algorithm 66

3.3 Hardware Implementation 74

3.3.1 PV Simulator 76

3.3.2 Driver Circuit 78

3.3.3 Voltage and Current Sensors 79

3.3.4 DC-DC Boost Converter 81

3.3.5 Digital Signal Processor (DSP) 82

3.4 Summary 82

4 RESULTS AND DISCUSSION 83

4.1 Introduction 83

4.2 Simulation Results 83

4.3 Experimental Results 94

4.4 Summary 126

5 CONCLUSION AND FUTURE WORK 127

5.1 Conclusion 127

5.2 Research Contributions 128

5.3 Future Works 129

REFERENCES 130

APPENDICES 142

BIODATA OF STUDENT 160

LIST OF PUBLICATIONS 161

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LIST OF TABLES

Table Page

2.1 Summary of MPPT algorithm under PS condition as discussed

throughout this thesis

53

3.1 Specifications of Solar KC40T at 1000 W/m2 and 25 ºC 58

3.2 Components of dc-dc boost converter 65

3.3 Mathematical analysis from the results based on the linear function 70

3.4 Components of the boost converter prototype 82

4.1 The simulation results for S1, S2 and S3 94

4.2 The experimental results for S1, S2 and S3 106

4.3 The experimental results for S1 under different irradiances 125

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LIST OF FIGURES

Figure Page

1.1 The general schematic of considered standalone system 2

1.2 I-V curve of PV system for different load lines 3

1.3 P-V curve of PV array under shaded condition 4

2.1 PV cell, module, and array 7

2.2 Creation of electron-hole in a PV cell 8

2.3 PV cell as an electrical energy source 9

2.4 (a) Monocrystalline and (b) polycrystalline solar panels 10

2.5 Equivalent circuit of a PV cell 11

2.6 Schematic diagrams of PV array configurations: (a) series, (b) SP,

(c) parallel, (d) TCT, (e) BL, and (f) HC

15

2.7 Series-parallel (SP) configuration of PV array 17

2.8 SP configuration of PV array under PSC 18

2.9 General schematic of power switching block 19

2.10 Controller for switching converter 20

2.11 (a) Boost dc-dc converter, (b) buck dc-dc converter, and (c) buck-

boost dc-dc converter

21

2.12 Boost converter (a) with ideal switch, and (b) practical

comprehension using MOSFET and diode

22

2.13 Boost converter circuit with (a) the switch is in position 1, and (b)

the switch is in position 2

23

2.14 Boost converter (a) voltage and (b) current waveforms 24

2.15 DC conversion ratio of the boost converter 25

2.16 The biggest rectangular belongs to the MPP 27

2.17 I-V curve with different resistive load values 28

2.18 Operating points and MPPs under different irradiances 29

2.19 Operating points for different irradiances under PSCs 30

2.20 Flowchart of P&O algorithm 31

2.21 Failure of the MPPT algorithm to find the GMPP 32

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2.22 Flowchart of (a) the POC method and (b) checking algorithm 33

2.23 P-V curve for the IC method 37

2.24 Tracking of the GMPP based on the linear function method for the

first case of the complicated scenario: (a) I-V curve and (b) P-V

curve

38

2.25 Tracking of the GMPP based on the linear function method for the

second case of the complicated scenario: (a) I-V curve and (b) P-V

curve

39

2.26 Fuzzy logic diagram 40

2.27 Structure of typical artificial neural network 42

2.28 General structure of typical MPPT-based ANN 43

2.29 General structure of PSO 44

2.30 Fibonacci search on the P-V curve: (a) first iteration and (b)

second iteration

48

2.31 P-V curve under PSC 50

3.1 Flowchart of the work 57

3.2 (a) I-V and (b) P-V curves of the PV module under different

irradiances

59

3.3 (a) I-V and (b) P-V curves of the PV array under different

irradiances

60

3.4 Variation of MPP by changing the duty cycle in the I-V curve 62

3.5 Operating points for different irradiances under uniform

conditions

63

3.6 Operating points for different irradiances under PSCs 64

3.7 Simulation circuit of dc-dc boost converter 65

3.8 MPPT algorithm block with (a) PWM block and (b) PWM

subsystem in details

66

3.9 Zones for the possible MPPs 68

3.10 The possible location of new reference voltage 71

3.11 Flowchart of the proposed algorithm 73

3.12 Change of dp/dv at both sides of MPP 74

3.13 Schematic of experiment prototype 75

3.14 Experimental setup for proposed PV system 75

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3.15 (a) Solar array simulator power supply 62100H-600S and (b)

connected computer

77

3.16 IGBT driver circuit in Proteus software 78

3.17 Driver circuit 79

3.18 Current sensor: (a) schematic and (b) hardware 80

3.19 Voltage sensor: (a) schematic and (b) hardware 81

3.20 eZdsp TMS320F28335 board 82

4.1 Configuration of system 84

4.2 (a) I-V and (b) P-V curves for the first scenario of shadowing 85

4.3 (a) I-V and (b) P-V curves for the second scenario of shadowing 86

4.4 (a) I-V and (b) P-V curves for the third scenario of shadowing 87

4.5 (a) I-V and (b) P-V curves for the fourth scenario of shadowing 88

4.6 (a) I-V and (b) P-V curves for the fifth scenario of shadowing 89

4.7 PV output powers for the scenario of Figure 4.2 91





4.12 P-V and I-V curves under PSC with (a) the detected GMPP by S1

and S3, and (b) the failed detected GMPP by S2

95


4.14 Performance of voltage and current for the scenario of Figure 4.12

by implementing the DSMPP method

96

4.15 P-V and I-V curves under PSC with (a) the detected GMPP by S1

and S3, and (b) the failed detected GMPP by S2

97




98

4.18 P-V and I-V curves under PSC with detected GMPP by S1, S2 and

S3

99


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100


S3

101




102


S3

103




104


S3

104




105

4.30 P-V and I-V curves under PSC for irradiances of (a) 1000 W/m2,

(b) 800 W/m2 and (c) 600 W/m

2

108

4.31 PV output powers for the scenario in Figure 4.30, for irradiances

of (a) 1000 W/m2, (b) 800 W/m

2 and (c) 600 W/m

2

108

4.32 Performance of voltage and current for the scenarios of Figures

(a) 4.30(a), (b) 4.30(b), and (c) 4.30(c), by implementing the

DSMPP method

109


(b) 800 W/m2 and (c) 600 W/m

2

111


of (a) 1000 W/m2, (b) 800 W/m

2 and (c) 600 W/m

2

112



DSMPP method

113


(b) 800 W/m2 and (c) 600 W/m

2

115


of (a) 1000 W/m2, (b) 800 W/m

2 and (c) 600 W/m

2116

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DSMPP method

117


(b) 800 W/m2 and (c) 600 W/m

2

119


of (a) 1000 W/m2, (b) 800 W/m

2 and (c) 600 W/m

2

120



DSMPP method

121

4.42 P-V and I-V curves under PSC for irradiances of (a) 1000 W/m2

and (b) 800 W/m2

123

4.43 PV output power for the scenario in Figure 4.42 for irradiances of

(a) 1000 W/m2 and (b) 800 W/m

2

124


(a) 4.32(a) and (b) 4.32(b), by implementing the DSMPP method

125

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LIST OF APPENDICES

Appendix Page

A Data sheet of the KC40T PV module 142

B Data sheet of PV Simulator 62100H-600S 144

C Data sheet of current sensor LEM-LA25NP 150

D Data sheet of voltage sensor LEM-LV25P 152

E Digital signal processor (DSP) 155

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LIST OF ABBREVIATIONS

ANN Artificial neural network

ACO Ant colony optimization

ADC Analog-to-Digital converter

BL Bridge-link

CV Constant voltage

CCM continuous conduction mode

CE Change of error

CCS Code Composer Studio

DSP Digital signal processor

DSMPP Dual search maximum power point

DC Direct current

DCM discontinuous conduction mode

DE Differential evaluation

FL Fuzzy logic

FLC Fuzzy logic controller

FS Fibonacci search

FA Flashing fireflies

GA Genetic algorithm

GMPP Global maximum power point

GTO gate-turn off thyristor

HC Honey-comb

IC Incremental conductance

IGBT insulated-gate bipolar transistor

LMPP Local maximum power point

MPP Maximum power point

MPPT Maximum power point tracking

MOSFET metal–oxide–semiconductor field-effect transistor

NB Negative big

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NM Negative medium

NS Negative Small

OC Open-circuit

PV Photovoltaic

P&O Perturb and observation

PSO Particle swarm optimization

PSCs Partially shaded conditions

PI proportional-integral

PID proportional-integral-derivative

POC Perturb, observe, and check

PB Positive big

PM Positive medium

PS Positive small

PCB Printed circuit board

PWM Pulse width modulation

SP Series-parallel

STC Standard test condition

SC Short-circuit

SRC Standard reporting condition

SOP Status of operation

TCT Total-cross-tied

ZE Zero

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CHAPTER 1

INTRODUCTION

1.1 Research Background

Fossil fuels are sources of non-renewable energy that are finite; as a result, the sources

of fossil fuels will eventually become depleted, resulting in a high cost of fuel while

also affecting the environment, in particularly global warming [1]. In contrast, there are

certain types of renewable energy resources, such as solar and wind energy, that are

continually resupplied and are virtually inexhaustible [2, 3]. Among renewable energy

resources, energy from the sun is commercially viable because of its potential for high

productivity and low emissions [4]. A photovoltaic (PV) system generates electricity by

the direct conversion of the sun’s energy into electricity [5]. This simple principle

involves advanced technology used to build efficient devices, namely solar cells, which

are the key components of a PV system and require semiconductor processing

techniques in order to be manufactured at low cost and high efficiency. In PV systems,

there are certain factors that can create power losses, such as current and voltage

mismatch [6, 7], the accumulation of dust on a PV module’s surface [8], the angle of

prevalence of such radiation, and the maximum power point (MPP) of a PV system.

So, by considering all the advantages and disadvantages of this source of energy, it is

necessary to increase the efficiency to become more commercial [9, 10].

The PV system can be categorized in three general types: standalone, grid connected,

and hybrid systems [11-13]. The standalone PV system includes dc-dc converter and

specific load such as water pump and lighting [14]. The grid connected PV system can

include dc-dc and dc-ac converters, in which the output current and voltage of the

system are connected directly to grid. Hybrid PV systems most commonly take the

form of PV systems combined with wind turbines or diesel generators. They would

most likely be found on island, yet they could also be built in other areas.

In this work, the standalone system is considered which includes the dc-dc boost

converter to increase the output voltage to supply the load. The PV system contains a

controller which performs maximum power point tracking (MPPT) algorithm. The

general schematic of system is shown Figure 1.1.

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Photovoltaic

Array

Power DC/DC

ConverterLoad

Voltage and Current

SensingPWM Generator

MPPT Algorithm

Figure 1.1: The general schematic of considered standalone system

PV systems are distinguished by their I-V and P-V characteristics, where I, V, and P are

the current, voltage, and power of the PV system, respectively. Each type of load

connected to a PV system has a load line characteristic. As shown in Figure 1.2, the

intersection point between the load line and I-V characteristic of a PV system defines

the operating point, which can be varied by changing the load value [15]. Thus, the

output power of a PV system, which is defined by multiplying the current by the

voltage, ranges from nearly zero to the maximum value of the PV system. To solve this

problem of variable power output and to simultaneously avoid further power losses, a

MPPT algorithm is used to determine the maximum available power of a PV system

which is as interface between the PV system and load [16, 17]. According to previous

studies, the PV system with MPP tracker is able to save 30%-40% more energy in

comparison with the system without tracker [18-20].

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Figure 1.2: I-V curve of PV system for different load lines

In recent years, many different MPPT algorithms [21] have been presented and can be

classified into two general groups. The first group is conventional methods, such as

perturb and observation (P&O) [22-25], incremental conductance (IC) [26, 27], sliding

mode [28, 29], and constant voltage (CV) [30, 31], and the second group is artificial

intelligence methods, such as fuzzy logic (FL) [32-36], artificial neural network (ANN)

[37, 38] , ant colony optimization (ACO) [39], genetic algorithm (GA) [40, 41], and

particle swarm optimization (PSO) [42, 43]. Among the conventional methods, P&O is

the most frequently used because it is easy to be implemented in PV system

controllers.

Under uniform conditions, in which there is only a single peak power point, generally,

all of the above-mentioned methods are successful in finding the MPP, and some of

them have their own particular advantages, such as a short time required to obtain the

MPP and acceptable with oscillations [44-46]. However, in some cases, especially

under partially shaded conditions (PSCs), there are multiple power peaks; one of these

peaks is the global maximum power point (GMPP), which has the highest power, and

the other peaks are local maximum power points (LMPPs). According to some studies

[47-49], the power loss can vary from 10 to 70% due to PSC. To determine the GMPP,

smart techniques should be combined with the above-mentioned methods. In Figure

1.3, the P-V curve with multiple peaks of power which includes the LMPPs and GMPP

is shown.

0 5 10 15 20 250

0.5

1

1.5

2

2.5

3

3.5

PV Voltage (V)

PV

Cu

rren

t (

A)

1/R

1/Ropt

Voc

IscNM

A

P

S

Vmpp

Impp

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Figure 1.3: P-V curve of PV array under shaded condition

1.2 Problem Statement

As mentioned earlier, the PV system suffers from nonlinearity between current and

voltage under PSC. In recent years, many researchers have presented different

strategies for finding the GMPP under shaded conditions. Generally, the presented

methods can be categorized in two general groups. The first group is hybrid methods

like two stage methods which are based on combination of classical methods with

other techniques, and the second group is the methods based on artificial intelligent

methods. In the first group, those methods usually look for all possible MPPs by

scanning the whole P-V curve, which increases the searching time [50, 51], especially

in the PV systems with large number of the modules in strings, and subsequently

increases the power loss as well [52].

The second group is based on artificial intelligent methods such as ANN, FL, PSO, and

ant colony optimization (ACO). These methods are quite efficient, but each has its own

drawbacks. For example, ANN must provide enough experimental data to be trained.

In the FL method, there are certain primary components, such as fuzzification and

defuzzification that require large computational memory; in addition, the specific range

of membership functions and rules should be varied according to the specific

application. The PSO method is more useful in large PV systems with a large number

of strings, but this method requires experience for setting the parameters. In some

works, specific conditions are assumed for designing a new MPP algorithm and only

specific P-V and I-V curves are considered. In MPPT methods based on PSO, by

increasing number of the particles [53], the accuracy can be increased, but it leads to

increasing of the calculation burden. The complexity of the PSO method is another

disadvantage of MPPT; in reality, the practical controllers such as microcontroller and

0 20 40 60 80 100 1200

100

200

300

400

500

600

700

800

900

PV Array Voltage (V)

PV

Arra

y P

ow

er (

W)

Under uniform condition

Under partially shaded condition

LMPP

LMPP

LMPP

MPP

GMPP

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digital signal processor (DSP) need to have bigger memory if the algorithm is

complicated. Another artificial intelligent method used for MPPT is the ACO method,

also has its own disadvantage. In this method [39], if the generated ants are distant

from the GMPP, the likelihood of failing in detecting the GMPP is very high.

Therefore, by considering all advantages and drawbacks of the above mentioned

existing methods, finding a new method which can reduce searching zone and

consequently identifying the GMPP in the minimum amount of time is essential.

1.3 Aim and Objectives

The main aim of this work is to design a novel hybrid method which is called dual

search maximum power point (DSMPP) algorithm for a standalone PV system to

detect the GMPP under PSCs. The detailed objectives in order to achieve the aim are

listed as follows:

1. To model PV array in series-parallel (SP) configuration and to design dc-dc

boost converter for the considered PV array.

2. To design, develop, and integrate a novel MPPT algorithm which can detect

the GMPP under PSCs.

3. To simulate and evaluate performance of the MPPT algorithm with the PV

array under various PSCs in terms of to reach the GMPP, oscillation in power

and accuracy.

4. To experimentally validate the MPPT controller in terms of time to reach the

GMPP, oscillation in power and accuracy.

The proposed MPPT algorithm is implemented in the control unit of the PV system and

its performance is evaluated by simulation in MATLAB/Simulink and then tested and

verified in the laboratory by developing the experiment prototype.

1.4 Scope and Limitations

This work aims to simulate and implement a novel hybrid MPPT algorithm for PV

system operating under PSCs. The considered PV module in this work is based on

KC40T PV modules connected in the SP configuration. In actual PV power plants, the

SP configuration is the most common connection since there are advantages to both

series and parallel connections. In recent years, different topologies have been

presented but most of them rely on SP and total-cross-tied (TCT) configurations. The

main important note for dealing with the re-configurable topologies of the PV array is

the number of switches and sensors used in the selected configuration. In SP

configuration, the number of switches is much lower than TCT configuration. In the

considered PV array, one diode is connected in parallel with each PV module to avoid

the hotspot phenomenon and also to reduce the effect of mismatch which leads to

increase output power of the system.

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For practical validation, the programmable solar array simulator power supply

62100H-600S series is used to generate I-V and P-V curves under PSCs. This solar

array simulator provides simulation of open-circuit voltage up to 1000 V and short-

circuit current up to 25 A. The dc-dc boost converter is used as interface between PV

array and load and then, the whole system is simulated, tested and verified under PSCs.

The temperature effect is neglected since change of temperature is very slow in

comparison with change of irradiance.

1.5 Thesis Organization

The remainder of this thesis is organized as follows:

Chapter 2 presents an overview on PV system which is describing the PV cell,

equivalent circuit of PV cell, and mathematical model of PV array under uniform and

PS conditions. After that, a comprehensive study of boost, buck, and buck-boost

converters in order to select the proper topology for this work has been done. Finally,

the different MPPT algorithms and further analyses under PSC are explained.

Chapter 3 describes the methodology of the work which includes the mathematical

model of the PV module and array under uniform and PSCs, development of the dc-dc

boost converter for the considered system, design of the proposed method by

describing the mathematical analysis, explanation on the PV simulator, and description

on implementing experiment setup and operation of DSP via Simulink/MATLAB for

the proposed method.

Chapter 4 presents the simulation results for the completed PV system by

implementing the proposed method which has been done via Simulink/MATLAB.

Then, the experimental setup with details of experiment design and implementation are

presented. Finally, the experimental results for the proposed method by comparing

with other methods are presented.

Chapter 5 summarizes the findings obtained from implementation of the proposed

method, and also recommends the potential future works.

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REFERENCES

[1] R. Banos, F. Manzano-Agugliaro, F. Montoya, C. Gil, A. Alcayde, and J.

Gómez. 2011. Optimization methods applied to renewable and sustainable

energy: A review. Renewable and Sustainable Energy Reviews,15 : 1753-

1766.

[2] J. Bratt. 2011. Grid connected PV inverters: Modeling and Simulation. San

Diego State University.

[3] T. Bennett, A. Zilouchian, and R. Messenger. 2012. Photovoltaic model and

converter topology considerations for MPPT purposes. Solar energy, 86:

2029-2040.

[4] K. Tomabechi. 2010. Energy resources in the future. Energies, 3: 686-695.

[5] W. Xiao. 2003. A modified adaptive hill climbing maximum power point

tracking (MPPT) control method for photovoltaic power systems. Master

thesis, Universiti British Columbia.

[6] G. Liu, S. K. Nguang, and A. Partridge. 2011. A general modeling method for

I–V characteristics of geometrically and electrically configured photovoltaic

arrays. Energy Conversion and Management, 52: 3439-3445.

[7] S. Shirzadi, H. Hizam, and N. I. A. Wahab. 2014. Mismatch losses

minimization in photovoltaic arrays by arranging modules applying a genetic

algorithm. Solar energy, 108: 467-478.

[8] H. K. Elminir, A. E. Ghitas, R. Hamid, F. El-Hussainy, M. Beheary, and K.

M. Abdel-Moneim. 2006. Effect of dust on the transparent cover of solar

collectors. Energy Conversion and Management, 47: 3192-3203.

[9] A. Pandey, N. Dasgupta, and A. K. Mukerjee. 2008. High-performance

algorithms for drift avoidance and fast tracking in solar MPPT system. IEEE

Transactions on Energy Conversion, 23: 681-689.

[10] D. Torres Lobera. 2011. Measuring actual operating conditions of a

photovoltaic power generator. Master thesis, Universiti Tampere of

Technology.

[11] B. G. Belgacem. 2012. Performance of submersible PV water pumping

systems in Tunisia. Energy for Sustainable Development, 16: 415-420.

[12] G. Delvecchio, M. Guerra, C. Lofrumento, and F. Neri. 2005. A Study for

Optimizing a Stand-Alone Hybrid Photovoltaic-Diesel System to Feed

Summer Loads. In International Conference on Renewable Energy and Power

Quality, ICREPQ, Spain, 167-168.

[13] N. Chayawatto, K. Kirtikara, V. Monyakul, C. Jivacate, and D. Chenvidhya.

2009. DC–AC switching converter modelings of a PV grid-connected system

under islanding phenomena. Renewable Energy, 34: 2536-2544.

[14] S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg. 2002. Power inverter topologies

for photovoltaic modules-a review. In Industry Applications Conference,

2002. 37th IAS Annual Meeting. Conference Record of the, 782-788.

[15] S. Balakrishna, A. Nabil, G. Rajamohan, A. Kenneth, and C. Ling. 2006. The

Study and Evaluation of Maximum Power Point Tracking Systems.

[16] J. Applebaum. 1987. The quality of load matching in a direct-coupling

photovoltaic system. IEEE Transactions on Energy Conversion, 534-541.

[17] H. T. Duru. 2006. A maximum power tracking algorithm based on I mpp= f

(P max) function for matching passive and active loads to a photovoltaic

generator. Solar energy, 80: 812-822.

© COPYRIG

HT UPM

131

[18] R. Gules, J. De Pellegrin Pacheco, H. L. Hey, and J. Imhoff. 2008. A

maximum power point tracking system with parallel connection for PV stand-

alone applications. IEEE Transactions on Industrial Electronics, 55: 2674-

2683.

[19] D. Hohm and M. E. Ropp. 2003. Comparative study of maximum power point

tracking algorithms. Progress in Photovoltaics: Research and Applications,

11: 47-62.

[20] K. Hussein, I. Muta, T. Hoshino, and M. Osakada. 1995. Maximum

photovoltaic power tracking: an algorithm for rapidly changing atmospheric

conditions. IEE Proceedings-Generation, Transmission and Distribution, 142:

59-64.

[21] T. Esram and P. L. Chapman. 2007. Comparison of photovoltaic array

maximum power point tracking techniques. IEEE Transactions on Energy

Conversion EC, 22: 439, 2007.

[22] D. Hohm and M. Ropp. 2000. Comparative study of maximum power point

tracking algorithms using an experimental, programmable, maximum power

point tracking test bed. In Photovoltaic specialists conference, 2000.

Conference record of the twenty-eighth IEEE, 1699-1702.

[23] I. Houssamo, F. Locment, and M. Sechilariu. 2010. Maximum power tracking

for photovoltaic power system: Development and experimental comparison of

two algorithms. Renewable Energy, 35: 2381-2387.

[24] J. Jiang, T. Huang, Y. Hsiao, and C. Chen. 2005. Maximum power tracking

for photovoltaic power systems. Tamkang Journal of Science and

Engineering, 8: 147-153.

[25] F. Esposito, V. Isastia, S. Meo, and L. Piegari. 2008. An improved perturbe

and observe algorithm for tracking maximum power points of photovoltaic

power systems. International Review on Modelling and Simulations

(IREMOS), 10-16.

[26] C. Hua and C. Shen. 1998. Study of maximum power tracking techniques and

control of DC/DC converters for photovoltaic power system. In Power

Electronics Specialists Conference, 1998. PESC 98 Record. 29th Annual

IEEE, 86-93.

[27] B. K. Bose, P. M. Szczesny, and R. L. Steigerwald. 1985. Microcomputer

control of a residential photovoltaic power conditioning system. Industry

Applications, IEEE Transactions on, IA-21: 1182-1191.

[28] M. A. Orozco, J. Vázquez, and P. Salmerón. 2010. MPP Tracker of a PV

System using Sliding Mode Control with Minimum Transient Response.

International Review on Modelling and Simulations, 3.

[29] E. Bianconi, J. Calvente, R. Giral, E. Mamarelis, G. Petrone, C. A. Ramos-

Paja, G. Spagnuolo, and M. Vitelli. 2013. Perturb and observe MPPT

algorithm with a current controller based on the sliding mode. International

Journal of Electrical Power & Energy Systems, 44: 346-356.

[30] K. Aganah and A. W. Leedy. 2011. A constant voltage maximum power point

tracking method for solar powered systems. In System Theory (SSST), 2011

IEEE 43rd Southeastern Symposium on, 2011, 125-130.

[31] M. A. Masoum, H. Dehbonei, and E. F. Fuchs. 2002. Theoretical and

experimental analyses of photovoltaic systems with voltageand current-based

maximum power-point tracking. IEEE Transactions on Energy

Conversion,17: 514-522.

© COPYRIG

HT UPM

132

[32] M. M. Algazar, H. A. EL-halim, and M. E. E. K. Salem. 2012. Maximum

power point tracking using fuzzy logic control. International Journal of

Electrical Power & Energy Systems, 39: 21-28.

[33] J.-K. Shiau, Y.-C. Wei, and M.-Y. Lee. 2015. Fuzzy controller for a voltage-

regulated solar-powered MPPT system for hybrid power system applications.

Energies, 8: 3292-3312.

[34] A. H. El Khateb, N. A. Rahim, and J. Selvaraj. 2013. Fuzzy logic control

approach of a maximum power point employing SEPIC converter for

standalone photovoltaic system. Procedia Environmental Sciences, 17: 529-

536.

[35] O. Guenounou, B. Dahhou, and F. Chabour. 2014. Adaptive fuzzy controller

based MPPT for photovoltaic systems. Energy Conversion and Management,

78: 843-850.

[36] C.-L. Liu, J.-H. Chen, Y.-H. Liu, and Z.-Z. Yang. 2014. An asymmetrical

fuzzy-logic-control-based MPPT algorithm for photovoltaic systems.

Energies, 7: 2177-2193.

[37] T. Hiyama and K. Kitabayashi. 1997. Neural network based estimation of

maximum power generation from PV module using environmental

information. IEEE Transactions on Energy Conversion, 12: 241-247.

[38] A. Chaouachi, R. M. Kamel, and K. Nagasaka. 2010. A novel multi-model

neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system.

Solar energy, 84: 2219-2229.

[39] L. L. Jiang, D. L. Maskell, and J. C. Patra. 2013. A novel ant colony

optimization-based maximum power point tracking for photovoltaic systems

under partially shaded conditions. Energy and Buildings, 58: 227-236.

[40] Y. Shaiek, M. B. Smida, A. Sakly, and M. F. Mimouni. 2013. Comparison

between conventional methods and GA approach for maximum power point

tracking of shaded solar PV generators. Solar energy, 90: 107-122.

[41] A. Messai, A. Mellit, A. Guessoum, and S. Kalogirou. 2011. Maximum power

point tracking using a GA optimized fuzzy logic controller and its FPGA

implementation. Solar energy, 85: 265-277.

[42] M. Sarvi, S. Ahmadi, and S. Abdi. 2015. A PSO‐based maximum power point

tracking for photovoltaic systems under environmental and partially shaded

conditions. Progress in Photovoltaics: Research and Applications, 23: 201-

214.

[43] M. Miyatake, M. Veerachary, F. Toriumi, N. Fujii, and H. Ko. 2011.

Maximum power point tracking of multiple photovoltaic arrays: a PSO

approach. IEEE Transactions on Aerospace and Electronic Systems, 47: 367-

380.

[44] E. Liedholm. 2010. Tracking the maximum power point of solar panels. A

digital implementation using only voltage measurements, Master Thesis,

Chalmers Universiti of Technology.

[45] X. Zhang. 2011. Control Strategy of Cascaded H-Bridge Multilevel Inverter

with PV system as Separate DC Source, Master Thesis, KTH University.

[46] S. S. Kumar, G. Dharmireddy, P. Raja, and S. Moorthi. 2011. A voltage

controller in photo-voltaic system without battery storage for Stand-Alone

Applications. In Electrical, Control and Computer Engineering (INECCE),

2011 International Conference on, 269-274.

[47] C. R. Sullivan, J. J. Awerbuch, and A. M. Latham. 2013. Decrease in

photovoltaic power output from ripple: Simple general calculation and the

© COPYRIG

HT UPM

133

effect of partial shading. IEEE Transactions on Power Electronics, 28: 740-

747.

[48] E. Karatepe, T. Hiyama, M. Boztepe, and M. Çolak. 2008. Voltage based

power compensation system for photovoltaic generation system under

partially shaded insolation conditions. Energy Conversion and Management,

49: 2307-2316.

[49] F. Martínez-Moreno, J. Muñoz, and E. Lorenzo. 2010. Experimental model to

estimate shading losses on PV arrays. Solar energy materials and solar cells,

94: 2298-2303.

[50] K. Kobayashi, I. Takano, and Y. Sawada. 2006. A study of a two stage

maximum power point tracking control of a photovoltaic system under

partially shaded insolation conditions. Solar energy materials and solar cells,

90: 2975-2988.

[51] H. Patel and V. Agarwal. 2008. Maximum power point tracking scheme for

PV systems operating under partially shaded conditions. IEEE Transactions

on Industrial Electronics, 55: 1689-1698.

[52] R. Alonso, P. Ibaez, V. Martinez, E. Roman, and A. Sanz. 2009. An

innovative perturb, observe and check algorithm for partially shaded PV

systems, In Power Electronics and Applications, EPE'09. 13th European

Conference on, 1-8.

[53] S. R. Chowdhury and H. Saha. 2010. Maximum power point tracking of

partially shaded solar photovoltaic arrays. Solar energy materials and solar

cells, 94: 1441-1447.

[54] L. Castaner and S. Silvestre. 2002. Front Matter: Wiley Online Library.

[55] S. M. Sze and K. K. Ng. 2006. United States: Physics of semiconductor

devices: John Wiley & Sons. New jersey, Wiley Interscience.

[56] G. M. Masters. 2013. United States: Renewable and efficient electric power

systems: John Wiley & Sons. New jersey, Wiley Interscience.

[57] D. M. Chapin, C. Fuller, and G. Pearson. 1954. A new silicon p‐n junction

photocell for converting solar radiation into electrical power. Journal of

Applied Physics, 676-677.

[58] H. J. Möller. 1993. Semiconductors for solar cells, Artech House. Inc.,

Boston, MA, 187.

[59] J. Yang, A. Banerjee, and S. Guha. 2003. Amorphous silicon based

photovoltaics—from earth to the “final frontier”. Solar energy materials and

solar cells, 78: 597-612.

[60] C. Lund, K. Luczak, T. Pryor, J. Cornish, P. Jennings, P. Knipe, and F.

Ahjum. 2001. Field and laboratory studies of the stability of amorphous

silicon solar cells and modules. Renewable Energy, 22: 287-294.

[61] Y. Tawada and H. Yamagishi. 2001. Mass-production of large size a-Si

modules and future plan. Solar energy materials and solar cells, 66: 95-105.

[62] A. G. Aberle. 2001. Overview on SiN surface passivation of crystalline silicon

solar cells. Solar energy materials and solar cells, 65: 239-248.

[63] M. Lipiński, P. Panek, Z. Świątek, E. Bełtowska, and R. Ciach. 2002. Double

porous silicon layer on multi-crystalline Si for photovoltaic application. Solar

energy materials and solar cells, 72: 271-276.

[64] L. Dobrzański and A. Drygała. 2007. Laser processing of multicrystalline

silicon for texturization of solar cells," Journal of Materials Processing

Technology, 191: 228-231.

© COPYRIG

HT UPM

134

[65] S. Messina, M. Nair, and P. Nair. 2007. Antimony sulfide thin films in

chemically deposited thin film photovoltaic cells. Thin Solid Films, 515:

5777-5782.

[66] D. S. Morales. 2010. Maximum power point tracking algorithms for

photovoltaic applications, Master Thesis, AALOT Universiti.

[67] E. Lee, S. J. Park, J. W. Cho, J. Gwak, M.-K. Oh, and B. K. Min. 2011.

Nearly carbon-free printable CIGS thin films for solar cell applications. Solar


[68] S. Panwar and R. Saini. 2012. Development and Simulation of Solar

Photovoltaic model using Matlab/simulink and its parameter extraction. In

International Conference on Computing and Control Engineering (ICCCE

2012).

[69] G. Walker. 2001. Evaluating MPPT converter topologies using a MATLAB

PV model. Journal of Electrical & Electronics Engineering, Australia, 21:49-

56.

[70] N. Pandiarajan and R. Muthu. 2011. Mathematical modeling of photovoltaic

module with Simulink. In Proceeding of International Conference on

Electrical Energy System, 3-5.

[71] H.-L. Tsai, C.-S. Tu, and Y.-J. Su. 2008. Development of generalized

photovoltaic model using MATLAB/SIMULINK. In Proceedings of the

world congress on engineering and computer science, 1-6.

[72] M. G. Villalva and J. R. Gazoli. 2009. Comprehensive approach to modeling

and simulation of photovoltaic arrays. IEEE Transactions on Power

Electronics, 24: 1198-1208.

[73] S. Cells. 1982. Operating Principles, Technology, and System Applications.

ed: Prentice-Hall series in solid state physical electronics) by Martin A.

Green.

[74] S. Chowdhury, S. Chowdhury, G. Taylor, and Y. Song. 2008. Mathematical

modelling and performance evaluation of a stand-alone polycrystalline PV

plant with MPPT facility. In Power and Energy Society General Meeting-

Conversion and Delivery of Electrical Energy in the 21st Century, 2008

IEEE, 1-7.

[75] J.-H. Jung and S. Ahmed. 2010. Model construction of single crystalline

photovoltaic panels for real-time simulation. In Energy Conversion Congress

and Exposition (ECCE), 2010 IEEE, 342-349.

[76] F. M. González-Longatt. 2005. Model of photovoltaic module in Matlab. II

CIBELEC, 2005: 1-5.

[77] R. A. Messenger and J. Ventre. 2010. Photovoltaic systems engineering: CRC

press.

[78] N. Hatziargyriou, M. Donnelly, S. Papathanassiou, J. P. Lopes, M. Takasaki,

H. Chao, J. Usaola, R. Lasseter, A. Efthymiadis, and K. Karoui. 2000.

Modeling new forms of generation and storage. Cigre TF38, 1.

[79] A. M. Bazzi. 2007. Maximum power point tracking of multiple photovoltaic

arrays-by Ali Mohamad Bazzi, Master Thesis, American University of Beirut.

[80] S. W. Angrist. 1976. United States: Direct energy conversion. Boston, Allyn

and Bacon.

[81] J. Gow and C. Manning. 1999. Development of a photovoltaic array model for

use in power-electronics simulation studies. In Electric Power Applications,

IEE Proceedings-, 193-200.

© COPYRIG

HT UPM

135

[82] Y.-H. Ji, J.-G. Kim, S.-H. Park, J.-H. Kim, and C.-Y. Won. 2009. C-language

based PV array simulation technique considering effects of partial shading. In

Industrial Technology, ICIT 2009. IEEE International Conference on, 1-6.

[83] V. Quaschning and R. Hanitsch. 1996. Numerical simulation of current-

voltage characteristics of photovoltaic systems with shaded solar cells. Solar

energy, 56: 513-520.

[84] N. D. Kaushika and N. K. Gautam. 2003. Energy yield simulations of

interconnected solar PV arrays. IEEE Transactions on Energy Conversion,

18: 127-134.

[85] N. Shepard and R. Sugimura. 1984. The integration of bypass diodes with

terrestrial photovoltaic modules and arrays. In Conf. Rec. IEEE Photovoltaic

Spec. Conf.;(United States).

[86] F. Giraud and Z. M. Salameh. 1999. Analysis of the effects of a passing cloud

on a grid-interactive photovoltaic system with battery storage using neural

networks. IEEE Transactions on Energy Conversion, 14: 1572-1577.

[87] I. M. Syed and A. Yazdani. 2014. Simple mathematical model of photovoltaic

module for simulation in Matlab/Simulink. In Electrical and Computer

Engineering (CCECE), 2014 IEEE 27th Canadian Conference on, 1-6.

[88] R. Ramabadran and B. Mathur. 2009. Effect of shading on series and parallel

connected solar PV modules. Modern Applied Science, 3: 32-42.

[89] G. Petrone, G. Spagnuolo, and M. Vitelli. 2007. Analytical model of

mismatched photovoltaic fields by means of Lambert W-function. Solar


[90] G. Petrone and C. Ramos-Paja. 2011. Modeling of photovoltaic fields in

mismatched conditions for energy yield evaluations. Electric Power Systems

Research, 81: 1003-1013.

[91] J. Bastidas, C. Ramos-Paja, E. Franco, G. Spagnuolo, and G. Petrone. 2012.

Modeling of photovoltaic fields in mismatching conditions by means of

inflection voltages. In Engineering Applications (WEA), 2012 Workshop on,

2012,1-6.

[92] H. Patel and V. Agarwal. 2008. MATLAB-based modeling to study the

effects of partial shading on PV array characteristics. IEEE Transactions on

Energy Conversion, 23: 302-310.

[93] C. A. Ramos-Paja, J. D. Bastidas, A. J. Saavedra-Montes, F. Guinjoan-

Gispert, and M. Goez. 2012. Mathematical model of total cross-tied

photovoltaic arrays in mismatching conditions. In Circuits and Systems

(CWCAS), 2012 IEEE 4th Colombian Workshop on, 2012, 1-6.

[94] D. Picault, B. Raison, S. Bacha, J. Aguilera, and J. De La Casa. 2010.

Changing photovoltaic array interconnections to reduce mismatch losses: a

case study. In Environment and Electrical Engineering (EEEIC), 2010 9th

International Conference on, 2010, 37-40.

[95] C. A. RAMOS-PAJA, J. D. BASTIDAS, and A. J. SAAVEDRA-MONTES.

2013. Experimental validation of a model for photovoltaic arrays in total

cross-tied configuration. Dyna, 80: 191-199.

[96] D. Radianto, D. A. Asfani, and T. Hiyama. 2012. Partial Shading Detection

and MPPT Controller for Total Cross Tied Photovoltaic using ANFIS.

ACEEE Int. J. on Electrical and Power Engineering, 3.

[97] M. S. El-Dein, M. Kazerani, and M. Salama. 2013. An optimal total cross tied

interconnection for reducing mismatch losses in photovoltaic arrays. IEEE

Transactions on Sustainable Energy, 4: 99-107.

© COPYRIG

HT UPM

136

[98] Y.-J. Wang and P.-C. Hsu. 2011. An investigation on partial shading of PV

modules with different connection configurations of PV cells. Energy, 36:

3069-3078.

[99] G. Velasco-Quesada, F. Guinjoan-Gispert, R. Piqué-López, M. Román-

Lumbreras, and A. Conesa-Roca. 2009. Electrical PV array reconfiguration

strategy for energy extraction improvement in grid-connected PV systems.

IEEE Transactions on Industrial Electronics, 56: 4319-4331.

[100] E. Karatepe and T. Hiyama. 2010. Simple and high-efficiency photovoltaic

system under non-uniform operating conditions. Renewable Power

Generation, IET, 4: 354-368.

[101] S. Ruddin, E. Karatepe, and T. Hiyama. 2009. Artificial neural network-polar

coordinated fuzzy controller based maximum power point tracking control

under partially shaded conditions. Renewable Power Generation, IET, 3: 239-

253.

[102] Y.-J. Wang and P.-C. Hsu. 2009. Analysis of partially shaded PV modules

using piecewise linear parallel branches model. World Academy of Science,

Engineering and Technology, 60: 783-789.

[103] M. Balato, L. Costanzo, and M. Vitelli. 2015. Series–Parallel PV array re-

configuration: Maximization of the extraction of energy and much more.

Applied Energy, 159: 145-160.

[104] D. La Manna, V. L. Vigni, E. R. Sanseverino, V. Di Dio, and P. Romano.

2014. Reconfigurable electrical interconnection strategies for photovoltaic

arrays: A review. Renewable and Sustainable Energy Reviews, 33: 412-426.

[105] D. Nguyen and B. Lehman. 2008. An adaptive solar photovoltaic array using

model-based reconfiguration algorithm. IEEE Transactions on Industrial


[106] M. Alahmad, M. A. Chaaban, S. kit Lau, J. Shi, and J. Neal. 2012. An

adaptive utility interactive photovoltaic system based on a flexible switch

matrix to optimize performance in real-time. Solar energy, 86: 951-963.

[107] H. Obane, K. Okajima, T. Oozeki, and T. Ishii. 2012. PV system with

reconnection to improve output under nonuniform illumination.

Photovoltaics, IEEE Journal of, 2: 341-347.

[108] Z. M. Salameh and F. Dagher. 1990. The effect of electrical array

reconfiguration on the performance of a PV-powered volumetric water pump.

IEEE Transactions on Energy Conversion, 5: 653-658.

[109] J. P. Storey, P. R. Wilson, and D. Bagnall. 2013. Improved optimization

strategy for irradiance equalization in dynamic photovoltaic arrays. IEEE

Transactions on Power Electronics, 28: 2946-2956.

[110] J. Storey, P. R. Wilson, and D. Bagnall. 2014. The optimized-string dynamic

photovoltaic array. IEEE Transactions on Power Electronics, 29: 1768-1776.

[111] W. E. Newell. 1974. Power Electronics---Emerging from Limbo. IEEE

Transactions on Industry Applications, 7-11.

[112] R. Middlebrook. 1981. Power electronics: an emerging discipline. In IEEE

International Symposium on Circuits and Systems, 1981, 27-29.

[113] R. Middlebrook. 1981. Power electronics: topologies, modeling, and

measurement. In IEEE international symposium on circuits and systems,

1981, 27-29.

[114] S. Cuk and R. Middlebrook. 1981. Basics of switched-mode power

conversion: topologies, magnetics, and control. Advances in Switched-Mode

Power Conversion, 2: 279-310.

© COPYRIG

HT UPM

137

[115] N. Mohan. 1988. Power electronic circuits: An overview. In Industrial

Electronics Society, 1988. IECON'88. Proceedings., 14 Annual Conference of,

522-527.

[116] B. K. Bose. 1992. Power electronics-a technology review. Proceedings of the

IEEE, 80: 1303-1334.

[117] M. Nishihara. 1990. Power electronics diversity. In International Power

Electronics Conference, 1990, 21-28.

[118] R. W. Erickson and D. Maksimovic. 2007. Fundamentals of power

electronics. New York, Springer Science & Business Media.

[119] M. H. Rashid. 2003. Power electronics: circuits, devices, and applications.

India, Prentice Hall.

[120] M. Taghvaee, M. Radzi, S. Moosavain, H. Hizam, and M. H. Marhaban.

2013. A current and future study on non-isolated DC–DC converters for

photovoltaic applications," Renewable and Sustainable Energy Reviews, 17:

216-227.

[121] T. P. Nguyen. 2001. Solar panel maximum power point tracker. Department

of Computer Science & Electrical Engineering, 64.

[122] H.-P. Le. 2006. Single inductor multiple output (SIMO) DC-DC converter.

[123] M. H. Rashid. 2010. Power electronics handbook: devices, circuits and

applications. Florida, Elsevier.

[124] N. Mohan and T. M. Undeland, Power electronics: converters, applications,

and design, John Wiley & Sons.

[125] W. Xiao, N. Ozog, and W. G. Dunford. 2007. Topology study of photovoltaic

interface for maximum power point tracking. IEEE Transactions on Industrial


[126] E. Duran, J. Andujar, F. Segura, and A. Barragan. 2011. A high-flexibility DC

load for fuel cell and solar arrays power sources based on DC–DC converters.

Applied Energy, 88: 1690-1702.

[127] H.-C. Lu and T.-L. Shih. 2010. Design of DC/DC Boost converter with FNN

solar cell Maximum Power Point Tracking controller. In Industrial

Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, 802-

807.

[128] M. Veerachary, T. Senjyu, and K. Uezato. 2002. Voltage-based maximum

power point tracking control of PV system. IEEE Transactions on Aerospace

and Electronic Systems, 38: 262-270.

[129] D. Jahanbakhsh. 2012. Implementation of DC-DC converter with maximum

power point tracking control for thermoelectric generator applications, Master

Thesis, KTH Universiti.

[130] S. Anitha and S. B. J. Prabha. 2011. Artificial neural network based maximum

power point tracker for photovoltaic system. In Sustainable Energy and

Intelligent Systems (SEISCON 2011), International Conference on, 130-136.

[131] C. Ratsame and T. Tanitteerapan. 2011. An efficiency improvement boost

converter circuit for photovoltaic power system with maximum power point

tracking. In Control, Automation and Systems (ICCAS), 2011 11th

International Conference on, 2011, 1391-1395.

[132] N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli. 2009. A technique for

improving P&O MPPT performances of double-stage grid-connected

photovoltaic systems. IEEE Transactions on Industrial Electronics, 56: 4473-

4482.

© COPYRIG

HT UPM

138

[133] I. V. Banu, R. Beniuga, and M. Istrate. 2013. Comparative analysis of the

perturb-and-observe and incremental conductance MPPT methods. In

Advanced Topics in Electrical Engineering (ATEE), 2013 8th International

Symposium on, 2013,1-4.

[134] R. Faranda, S. Leva, and V. Maugeri. 2008. MPPT techniques for PV

systems: energetic and cost comparison. In Power and Energy Society

General Meeting-Conversion and Delivery of Electrical Energy in the 21st

Century, 2008 IEEE, 2008, 1-6.

[135] A. K. Abdelsalam, A. M. Massoud, S. Ahmed, and P. Enjeti. 2011. High-

performance adaptive perturb and observe MPPT technique for photovoltaic-

based microgrids," IEEE Transactions on Power Electronics, 26: 1010-1021.

[136] O. Wasynezuk. 1983. Dynamic behavior of a class of photovoltaic power

systems," IEEE Transactions on Power Apparatus and Systems, 3031-3037.

[137] A. Al-Amoudi and L. Zhang. 1988. Optimal control of a grid-connected PV

system for maximum power point tracking and unity power factor. In Power

Electronics and Variable Speed Drives, 1998. Seventh International

Conference on (Conf. Publ. No. 456), 80-85.

[138] R.-Y. Kim and J.-H. Kim. 2013. An improved global maximum power point

tracking scheme under partial shading conditions. In Journal of International

Conference on Electrical Machines and Systems, 2013, 65-68.

[139] K. Chen, S. Tian, Y. Cheng, and L. Bai. 2014. An improved MPPT controller

for photovoltaic system under partial shading condition. IEEE Transactions

on Sustainable Energy, 5: 978-985.

[140] A. Murtaza, M. Chiaberge, F. Spertino, D. Boero, and M. De Giuseppe. 2014.

A maximum power point tracking technique based on bypass diode

mechanism for PV arrays under partial shading. Energy and Buildings, 73:

13-25.

[141] S. Kazmi, H. Goto, O. Ichinokura, and H. J. Guo. An improved and very

efficient MPPT controller for PV systems subjected to rapidly varying

atmospheric conditions and partial shading. In Power Engineering

Conference, 2009. AUPEC 2009. Australasian Universities, 2009, 1-6.

[142] M. Lei, S. Yaojie, L. Yandan, B. Zhifeng, T. Liqin, and S. Jieqiong. 2011. A

high performance MPPT control method. In Materials for Renewable Energy

& Environment (ICMREE), 2011 International Conference on, 195-199.

[143] E. Koutroulis and F. Blaabjerg. 2012. A new technique for tracking the global

maximum power point of PV arrays operating under partial-shading

conditions. Photovoltaics, IEEE Journal of, 2: 184-190.

[144] A. Safari and S. Mekhilef. 2011. Simulation and hardware implementation of

incremental conductance MPPT with direct control method using cuk

converter," IEEE Transactions on Industrial Electronicsn, 58: 1154-1161.

[145] P. E. Kakosimos and A. G. Kladas. 2011. Implementation of photovoltaic

array MPPT through fixed step predictive control technique. Renewable

Energy, 36: 2508-2514.

[146] D. Lalili, A. Mellit, N. Lourci, B. Medjahed, and E. Berkouk. 2011. Input

output feedback linearization control and variable step size MPPT algorithm

of a grid-connected photovoltaic inverter. Renewable Energy, 36: 3282-3291.

[147] A. Safari and S. Mekhilef. 2011. Incremental conductance MPPT method for

PV systems. In Electrical and computer engineering (CCECE), 2011 24th

Canadian Conference on, 2011, 000345-000347.

© COPYRIG

HT UPM

139

[148] Y.-H. Ji, D.-Y. Jung, J.-G. Kim, J.-H. Kim, T.-W. Lee, and C.-Y. Won. 2011.

A real maximum power point tracking method for mismatching compensation

in PV array under partially shaded conditions. IEEE Transactions on Power


[149] L. Zadeh. 1965. Fuzzy logic and its applications. New York, NY.

[150] K. Ishaque, S. S. Abdullah, S. M. Ayob, and Z. Salam. 2010. Single input

fuzzy logic controller for unmanned underwater vehicle. Journal of Intelligent

and Robotic Systems, 59: 87-100.

[151] K. Ishaque, S. S. Abdullah, S. Ayob, and Z. Salam. 2011. A simplified

approach to design fuzzy logic controller for an underwater vehicle. Ocean

Engineering, vol. 38: 271-284.

[152] C.-Y. Won, D.-H. Kim, S.-C. Kim, W.-S. Kim, and H.-S. Kim. 1994. A new

maximum power point tracker of photovoltaic arrays using fuzzy controller.

In Power Electronics Specialists Conference, PESC'94 Record., 25th Annual

IEEE, 396-403.

[153] R. M. Hilloowala and A. M. Sharaf. 1992. A rule-based fuzzy logic controller

for a PWM inverter in photo-voltaic energy conversion scheme. In Industry

Applications Society Annual Meeting, 1992., Conference Record of the 1992

IEEE, 762-769.

[154] M. Ajaamoum, M. Kourchi, R. Alaoui, and L. Bouhouch. 2013. Fuzzy

controller to extract the maximum power of a photovoltaic system. In

Renewable and Sustainable Energy Conference (IRSEC), 2013 International,

141-146.

[155] D. Beriber and A. Talha. 2013. MPPT techniques for PV systems," in Power

Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth

International Conference on, 1437-1442.

[156] L. Donghui and Z. Xiaodan. 2011. Research on fuzzy controller for

photovoltaic power system," in Electric Information and Control Engineering

(ICEICE), 2011 International Conference on, 3506-3509.

[157] M. A. Cheikh, C. Larbes, G. T. Kebir, and A. Zerguerras. 2007. Maximum

power point tracking using a fuzzy logic control scheme. Revue des energies

Renouvelables, 10: 387-395.

[158] M. S. Ngan and C. W. Tan. 2011. A study of maximum power point tracking

algorithms for stand-alone photovoltaic systems. In Applied Power

Electronics Colloquium (IAPEC), 2011 IEEE, 22-27.

[159] A. Kalantari, A. Rahmati, and A. Abrishamifar. 2009. A faster maximum

power point tracker using peak current control. In Industrial Electronics &

Applications, 2009. ISIEA 2009. IEEE Symposium on,117-121.

[160] A. Mohamed and M. Hannan. 2009. Maximum Power Point Tracking in Grid

Connected PV system using a novel fuzzy logic controller. In Research and

Development (SCOReD), 2009 IEEE Student Conference on, 349-352.

[161] A. G. Abo-Khalil, D.-C. Lee, J.-W. Choi, and H.-G. Kim. 2006. Maximum

power point tracking controller connecting PV system to grid. Journal of

Power Electronics, 6: 226-234.

[162] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams. 2013. A

maximum power point tracking technique for partially shaded photovoltaic

systems in microgrids. IEEE Transactions on Industrial Electronics, 60:

1596-1606.

[163] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams. 2011. Fuzzy-

logic-control approach of a modified hill-climbing method for maximum

© COPYRIG

HT UPM

140

power point in microgrid standalone photovoltaic system. IEEE Transactions

on Power Electronics, 26: 1022-1030.

[164] K. Punitha, D. Devaraj, and S. Sakthivel. 2013. Development and analysis of

adaptive fuzzy controllers for photovoltaic system under varying atmospheric

and partial shading condition. Applied Soft Computing, 13: 4320-4332.

[165] M. Negnevitsky. 2005. Artificial intelligence: a guide to intelligent systems.

Pearson Education.

[166] E. Karatepe and T. Hiyama. 2012. Performance enhancement of photovoltaic

array through string and central based MPPT system under non-uniform

irradiance conditions. Energy Conversion and Management, 62:131-140.

[167] L. Jiang and D. Maskell. 2014. A simple hybrid MPPT technique for

photovoltaic systems under rapidly changing partial shading conditions. In

Photovoltaic Specialist Conference (PVSC), 2014 IEEE 40th, 0782-0787.

[168] R. C. Eberhart and J. Kennedy. 1995. A new optimizer using particle swarm

theory. In Proceedings of the sixth international symposium on micro machine

and human science, 39-43.

[169] K.and Z. Salam. 2013. A deterministic particle swarm optimization maximum

power point tracker for photovoltaic system under partial shading condition.

IEEE Transactions on Industrial Electronics, 60: 3195-3206.

[170] Y.-H. Liu, S.-C. Huang, J.-W. Huang, and W.-C. Liang. 2012. A particle

swarm optimization-based maximum power point tracking algorithm for PV

systems operating under partially shaded conditions. IEEE Transactions on

Energy Conversion, 27: 1027-1035.

[171] K. Lian, J. Jhang, and I. Tian. 2014. A maximum power point tracking

method based on perturb-and-observe combined with particle swarm

optimization. Photovoltaics, IEEE Journal of, 4: 626-633.

[172] L. Liu and C. Liu. 2013. A Novel Combined Particle Swarm Optimization and

Genetic Algorithm MPPT Control Method for Multiple Photovoltaic Arrays at

Partial Shading. Journal of Energy Resources Technology, 135: 012002.

[173] M. Dorigo, G. D. Caro, and L. M. Gambardella. 1999. Ant algorithms for

discrete optimization. Artificial life,5: 137-172.

[174] A. Colorni, M. Dorigo, and V. Maniezzo. 1991. Distributed optimization by

ant colonies. Proceedings of the first European conference on artificial life,

134-142.

[175] M. Dorigo. 1992. Optimization, learning and natural algorithms. PhD Thesis,

Politecnico di Milano Universiti.

[176] Mohajeri, H. R., Moghaddam, M. P., Shahparasti, M., & Mohamadian, M.

2012. Development a new algorithm for maximum power point tracking of

partially shaded photovoltaic arrays. In IEEE 20th Iranian Conference on

Electrical Engineering (ICEE2012), 489-494.

[177] M. Miyatake, T. Inada, I. Hiratsuka, H. Zhao, H. Otsuka, and M. Nakano.

2004. Control characteristics of a fibonacci-search-based maximum power

point tracker when a photovoltaic array is partially shaded. In Power

Electronics and Motion Control Conference. IPEMC 2004. The 4th

International, 816-821.

[178] N. A. Ahmed and M. Miyatake. 2008. A novel maximum power point

tracking for photovoltaic applications under partially shaded insolation

conditions. Electric Power Systems Research.,78: 777-784.

[179] R. Ramaprabha, M. Balaji, and B. Mathur. 2012. Maximum power point

tracking of partially shaded solar PV system using modified Fibonacci search

© COPYRIG

HT UPM

141

method with fuzzy controller. International Journal of Electrical Power &

Energy Systems, 43: 754-765.

[180] R. Storn and K. Price. 1995. Differential evolution-a simple and efficient

adaptive scheme for global optimization over continuous spaces, 3: ICSI

Berkeley.

[181] M. F. N. Tajuddin, S. M. Ayob, Z. Salam, and M. S. Saad. 2013. Evolutionary

based maximum power point tracking technique using differential evolution

algorithm. Energy and Buildings, 67: 245-252.

[182] K. S. Tey, S. Mekhilef, H.-T. Yang, and M.-K. Chuang. 2014. A differential

evolution based MPPT method for photovoltaic modules under partial shading

conditions. International Journal of Photoenergy, 2014.

[183] X.-S. Yang. 2010. Nature-inspired metaheuristic algorithms, United

Kingdom, Luniver press.

[184] Zhou, L., Chen, Y., Liu, Q., & Wu, J. 2012. Maximum power point tracking

(MPPT) control of a photovoltaic system based on dual carrier chaotic search.

J Control Theory Appl, 10(2), 244–250.

[185] K. Sundareswaran, S. Peddapati, and S. Palani. 2014. MPPT of PV systems

under partial shaded conditions through a colony of flashing fireflies. IEEE

Transactions on Energy Conversion, 29: 463-472.

[186] M. A. El Ela and J. Roger. 1984. Optimization of the function of a

photovoltaic array using a feedback control system.Solar cells, 13: 107-119.

[187] V. Salas, E. Olias, A. Barrado, and A. Lazaro. 2006. Review of the maximum

power point tracking algorithms for stand-alone photovoltaic systems. Solar


[188] Y. Oshiro, H. Ono, and N. Urasaki. 2011. A MPPT control method for stand-

alone photovoltaic system in consideration of partial shadow. In Power

Electronics and Drive Systems (PEDS), 2011 IEEE Ninth International

Conference on, 1010-1014.

[189] E. Skoplaki and J. Palyvos. 2009. On the temperature dependence of

photovoltaic module electrical performance: A review of efficiency/power

correlations. Solar energy, 83: 614-624.

[190] B. Kroposki, D. Myers, K. Emery, L. Mrig, C. Whitaker, and J. Newmiller.

1996. Photovoltaic module energy rating methodology development. In

Photovoltaic Specialists Conference, 1996., Conference Record of the Twenty

Fifth IEEE, 1311-1314.

[191] H. Packard. 2005. 2.0 Amp Output Current IGBT Gate Drive Optocoupler.

ed: Datasheet.

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